Search Results (121,332)

Solar flares are among the most powerful and dynamic events in the solar system, resulting from the sudden release of magnetic energy stored in the Sun’s atmosphere. These energetic bursts of electromagnetic radiation can release up to ${10}^{32}$ erg of energy, impacting space weather and posing risks to technological infrastructure and therefore require accurate forecasting of solar flare occurrences and intensities. This study evaluates the predictive performance of three machine learning algorithms—Random Forest (RF), k-Nearest Neighbors (kNN), and Extreme Gradient Boosting (XGBoost)—for classifying solar flares into four categories (B, C, M, X). Using 13 parameters of the SHARP dataset, the effectiveness of the models was evaluated in binary and multiclass classification tasks. The analysis utilized 8 principal components (PCs), capturing 95% of data variance, and 100 PCs, capturing 97.5% of variance. Our approach uniquely combines binary and multiclass classification with different levels of dimensionality reduction, an innovative methodology not previously explored in the context of solar flare prediction. Employing a 10-fold stratified cross-validation and grid search for hyperparameter tuning ensured robust model evaluation. Our findings indicate that RF and XGBoost consistently demonstrate strong performance across all metrics, benefiting significantly from increased dimensionality. The insights of this study enhance future research by optimizing dimensionality reduction techniques and informing model selection for astrophysical tasks. By integrating this newly acquired knowledge into future research, more accurate space weather forecasting systems can be developed, along with a deeper understanding of solar physics. Full article

Single-exposure high dynamic range (HDR) imaging is critical for applications such as automotive and surveillance cameras, where motion artifacts and light emitting diode (LED) flicker are significant challenges. Charge-splitting HDR imaging using multi-tap complementary metal-oxide-semiconductor (CMOS) image sensors (CIS) effectively mitigates these issues and offers programmable dynamic range extension, demonstrating significant potential for such applications. In this work, we present a model to describe the performance of the charge-splitting pixel. Then, we experimentally characterize and tune the performance of a 4-tap CIS. Through performance tuning, the image sensor achieves a single-exposure dynamic range (DR) of 126 dB. This represents an improvement of 16 dB over the previously reported 110 dB while maintaining a high signal-to-noise ratio (SNR), with a minimum transition SNR exceeding 30 dB. Full article

The rapid development of the Internet of Things (IoT) has imposed increasingly stringent power consumption requirements on receiver design. Unlike phase-locked loops (PLLs), free-running oscillators eliminate power-hungry loop circuitry. However, the inherent frequency offset of free-running oscillators introduces uncertainty in the intermediate frequency (IF), preventing the receiver from aligning with the desired channel. To address this, we present a reconfigurable channel receiver employing a free-running oscillator and frequency estimation for low-power IoT applications. The proposed receiver first captures a specific preamble sequence corresponding to the desired channel through multiple parallel sub-channels implemented in the digital baseband (DBB), which collectively cover the expected IF frequency range. The desired IF frequency is estimated using the proposed preamble-based frequency estimation (PBFE) algorithm. After frequency estimation, the receiver switches to a single-channel mode and tunes its passband center frequency to the estimated IF frequency, enabling high-sensitivity data reception. Measurement results demonstrate that the PBFE algorithm achieves reliable frequency estimation with a minimum IF signal-to-noise ratio (SNR) of 2 dB and an estimation error below 22 kHz. In single-channel mode, with a residual frequency offset of 30 kHz, an 8-point energy accumulation decoding scheme achieves a bit error rate (BER) of 10⁻³ at an IF SNR of 5.2 dB. Compared with the case of the original 50 kHz IF frequency offset, the required SNR is improved by 4.1 dB. Full article

This paper presents a 0–360° phase shifter in 28 nm FD-SOI CMOS technology, suitable for radar applications and mm-wave wireless communication systems, which adopt high-efficiency transmitter architectures. It exploits a novel switching vector modulator based on a double-balanced Gilbert cell, which guarantees high-resolution phase control while exhibiting inherently high robustness against process and temperature variations. The phase control is performed by merely changing the currents in the Gilbert cells using digitally controlled current generators. The proposed phase shifter operates at 39 GHz and provides RMS phase and gain errors of 2.7–4.7° and 0.3–0.5 dB, respectively, while drawing 13 mA from a 1 V supply voltage. Full article

Among wireless communication technologies underlying Internet of Things (IoT)-based smart buildings, IQRF (Intelligent Connectivity Using Radio Frequency) technology is a promising candidate due to its low power consumption, cost-effectiveness, and wide coverage. However, effectively modeling the propagation characteristics of IQRF in complex indoor environments for simple and accurate network deployment remains challenging, as architectural elements like walls and corners cause substantial signal attenuation and unpredictable propagation behavior. This study investigates the applicability of a site-specific modeling approach, originally developed for urban street canyons, to characterize peer-to-peer (P2P) IQRF links operating at 868 MHz in typical indoor scenarios, including line-of-sight (LoS), one-turn, and two-turn non-line-of-sight (NLoS) configurations. The received signal powers are compared with well-known empirical models, including international telecommunication union radio communication sector (ITU-R) P.1238-9 and WINNER II, and ray-tracing simulations. The results show that while ITU-R P.1238-9 achieves lower prediction error under LoS conditions with a root mean square error (RMSE) of 5.694 dB, the site-specific approach achieves substantially higher accuracy in NLoS scenarios, maintaining RMSE values below 3.9 dB for one- and two-turn links. Furthermore, ray-tracing simulations exhibited notably larger deviations, with RMSE values ranging from 7.522 dB to 16.267 dB and lower correlation with measurements. These results demonstrate the potential of site-specific modeling to provide practical, computationally efficient, and accurate insights for IQRF network deployment planning in smart building environments. Full article

Background/Objectives: In this work, a series of six new indazole-benzimidazole hybrids (M1–M6) were designed, synthesized, and fully characterized. The design of these compounds was based on the combination of two pharmacophoric units, indazole and benzimidazole, both known for their broad spectrum of biological activities. Methods: The molecular hybridization strategy was planned to combine these scaffolds through an effective synthetic pathway, using 6-nitroindazole, two 2-mercaptobenzimidazoles, and 1,3- or 1,5-dihaloalkanes as key precursors, affording the desired hybrids in good yields and with enhanced biological activity. Quantum chemical calculations were performed to investigate the structural, electronic, and electrostatic properties of M1–M6 molecules using Density Functional Theory (DFT) at the B3LYP/6-311++G(d,p) level. The antimicrobial activity efficacy of these compounds was assessed in vitro against four Gram-positive bacteria (Staphylococcus aureus, Enterococcus faecalis, Bacillus cereus, and Lactobacillus plantarum), four Gram-negative bacteria (Salmonella enteritidis, Escherichia coli, Campylobacter coli, Campylobacter jejuni), and four fungal strains (Saccharomyces cerevisiae, Candida albicans, Candida tropicalis, and Candida glabrata) using ampicillin and tetracycline as reference standard drugs. Results: Among the series, compound M6 exhibited remarkable antimicrobial activity, with minimum inhibitory concentrations (MIC) of 1.95 µg/mL against S. cerevisiae and C. tropicalis, and 3.90 µg/mL against S. aureus, B. cereus, and S. enteritidis, while the standards Ampicillin (AmB) (MIC ≥ 15.62 µg/mL) and Tetracycline (TET) (MIC ≥ 7.81 µg/mL) exhibited higher MIC values. To gain molecular insights into the compounds, an in silico docking study was performed to determine the interactions of M1–M6 ligands against the antimicrobial target beta-ketoacyl-acyl carrier protein (ACP) synthase III complexed with malonyl-COA (PDB ID: 1HNJ). Molecular modeling data provided valuable information on the structure-activity relationship (SAR) and the binding modes influencing the candidate ligand-protein recognition. Amino acid residues, such as Arg249, located in the solvent-exposed region, were essential for hydrogen bonding with the nitro group of the 6-nitroindazole moiety. Furthermore, polar side chains such as Asn274, Asn247, and His244 participated in interactions mediated by hydrogen bonding with the 5-nitrobenzimidazole moiety of these compound series. Conclusions: The hybridization of indazole and benzimidazole scaffolds produced compounds with promising antimicrobial activity, particularly M6, which demonstrated superior potency compared to standard antibiotics. Computational and docking analyses provided insights into the structure–activity relationships, highlighting these hybrids as potential candidates for antimicrobial drug development. Full article

Alzheimer’s disease (AD) is the leading cause of dementia and is often prefaced by mild cognitive impairment (MCI). Detection of AD-related changes via blood-based biomarkers would enable critical therapeutic interventions early in disease progression. Neuronal enriched extracellular vesicle (NEEV) miRNAs regulate peripheral genes as a response to early AD brain changes and hence may have biomarker potential. Plasma NEEVs were captured from plasma samples of Mexican Americans (MAs) and Non-Hispanic Whites (NHWs) using an antibody against the neuronal surface marker CD171. miRNAs isolated from NEEVs were sequenced and analyzed using miRDeep2/DEseq2 and QIAGEN RNA-seq portal for differential expression between cognitively impaired (CI) and cognitively unimpaired controls. hsa-miR-122-5p was significantly underrepresented in the CI group in both MAs and NHWs compared to the healthy control. Other population-specific miRNAs (MAs: hsa-miR-26a-5p, hsa-let-7f-5p, and hsa-miR-139-5p, NHWs: hsa-miR-133a-3p, hsa-miR-125b-5p, and hsa-miR-100-5p) identified may have biomarker potential in AD precision medicine. Some of these differentially expressed miRNAs were associated with key AD-related comorbidities such as APOE genotype, age, and metabolic burden and were predicted to target genes within NF-κB -regulated inflammatory pathways. Together, these findings suggest that dysregulated miRNA networks may serve as a mechanistic link between comorbidity burden and AD-related neuroinflammation and neurodegeneration. Full article

Endometriosis represents a prevalent gynaecological disorder, impacting around 10% of the female population and affecting as many as 50% of women who are facing challenges with infertility. The pathogenesis of the disease encompasses intricate processes such as the formation of adhesions, degradation of the extracellular matrix, angiogenesis, increased cell proliferation, impaired apoptosis, and dysregulation of the immune response. Although endometriosis is common, its precise etiology remains unidentified, despite various hypotheses being suggested. Recent findings underscore the significance of non-coding RNAs, specifically long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), which have been identified as important regulators in the development of endometriosis. This literature review integrates findings from various transcriptomic and molecular studies to distinguish between lncRNAs and miRNAs that are associated with direct pathogenic roles and those that simply represent altered gene expression profiles in endometriosis. Particular long non-coding RNAs, such as H19, MALAT1, and LINC01116, are associated with chromatin remodeling, functioning as competitive endogenous RNAs, and influencing critical signaling pathways. Concurrently, specific microRNAs, including the miR-200 family, miR-145, and let-7b, seem to govern processes like epithelial-to-mesenchymal transition, angiogenesis, and cell adhesion. The findings highlight the significant potential of non-coding RNAs to serve as biomarkers for diagnostic purposes and as innovative therapeutic targets. Subsequent research endeavours ought to focus on corroborating these findings and elucidating the specific pathogenic roles of these non-coding RNAs in the context of endometriosis. Full article

Non-destructive assessment of kiwifruit quality is critical for postharvest preservation and grading. This paper proposes a novel quantitative evaluation method for the kiwifruit comprehensive quality index (KCQI) during shelf life, based on hyperspectral imaging (HSI) combined with a one-dimensional convolutional neural network (1D-CNN). Hyperspectral images of two kiwifruit cultivars were acquired at four shelf-life stages using an HSI system, and six quality parameters were measured as reference standards. Based on correlation and factor analyses, five key parameters—soluble solids content, firmness, L*, b*, and chroma—were selected to construct the KCQI. Three spectral band selection methods and three modeling algorithms were compared, with the competitive adaptive reweighted sampling (CARS)–1D-CNN model yielding the highest prediction accuracy ($R_P^2$ = 0.82, RMSEP = 0.26, RPD_P = 2.39). Subsequently, a spatial distribution map was generated to visualize the KCQI. These results demonstrate the potential of the HSI–1D-CNN approach for accurate postharvest quality monitoring and intelligent grading. Full article

Recreation, aesthetic appreciation, identity, and spiritual values are among the cultural ecosystem services (CES) produced by long-distance historic and pilgrimage trails. However, it is still difficult to convert these experiential benefits into quantifiable economic flows. This study collected 560 valid responses from an in-field survey conducted along the Via degli Dei (Bologna–Florence). Robust visitor clusters were created using Gower dissimilarities, Partitioning Around Medoids (PAM), silhouette diagnostics, and Factor Analysis for Mixed Data (FAMD). Each cluster was then profiled according to seasonal patterns, information channels, individual-level, per-category expenditures (accommodation, food, transport, services, and equipment), as well as motivations. Four segments are identified—Student Campers (low-budget, peak-summer), Working-Age Male B&B Hikers (short stays, B&B), Young Women on Mixed Lodging (mixed accommodation), and Midlife Comfort-Seekers (higher spend, shoulder-season)—underpinning our spending, seasonality, and managerial implications. Student Campers had the lowest absolute expenditures, while Midlife Comfort-Seekers had the highest (median lodging €180; food €175). The study offers practical levers for route governance (targeted communications, low-impact lodging strategies, shoulder-season promotion) to improve local value capture while reducing environmental pressure by connecting typologies to monetary CES flows. The findings provide a reproducible model for implementing recreational CES on historical-cultural tours. Full article

This study is devoted to the detailed examination of the concept of $(a,b)$-deformation, defined for parameters $a\in \mathbb{R}$ and $b>0$. The analysis is conducted within the framework of Cauchy–Stieltjes kernel (CSK) families of probability measures, with particular attention given to the role of their variance functions (VFs). Using the VF as the main analytical tool, it is shown that the $(a,b)$-deformation of any measure belonging to the free Meixner family (FMF) remains within the same family. Moreover, the VF framework provides a powerful and flexible means for establishing new limit theorems associated with $(a,b)$-deformed free convolution. In particular, several novel limiting behaviors are derived, which naturally encompass both free and Boolean additive convolutions as special cases. Full article

This study investigates the performance, emissions, and physicochemical characteristics of a small-scale gas turbine fueled with Jet A and camelina biodiesel blends (B10, B20, and B30). The blends were characterized by slightly higher density (up to +3%), viscosity (+12–18%), and lower heating value (−7–9%) compared to Jet A. These fuel properties influenced the combustion behavior and overall turbine response. Experimental results showed that exhaust gas temperature decreased by 40–60 °C and specific fuel consumption (SFC) increased by 5–8% at idle, while thrust variation remained below 2% across all operating regimes. Fuel flow was reduced by 4–9% depending on the blend ratio, confirming efficient atomization despite the higher viscosity. Emission measurements indicated a 20–30% reduction in SO₂ and a 10–35% increase in CO at low load, mainly due to the sulfur-free composition and lower combustion temperature of biodiesel. Transient response analysis revealed that biodiesel blends mitigated overshoot and undershoot amplitudes during load changes, improving combustion stability. Overall, the results demonstrate that camelina biodiesel–Jet A blends up to 30% ensure stable turbine operation with quantifiable environmental benefits and minimal performance penalties, confirming their suitability as sustainable aviation fuels (SAFs). Full article

Internal reference genes are a prerequisite for ensuring the accuracy of gene verification experiments, but few relevant studies on Lyophyllum decastes have investigated the growth cycle and different environmental conditions. In this study, the qPCR results of 22 house-keeping genes were analyzed using GeNorm, BestKeeper, NormFinder and RefFinder. The results revealed that the most stable gene differed under different conditions. Across all developmental stages and under hot, cold, acidic, alkaline, and salt conditions, UBCE gene displays the greatest expression stability. However, EF1b, β-ACT, HSD17B3, and Cyb presented the greatest stability under cold, heat, and acidic conditions, and heavy metal exposure, respectively. To screen for genes suitable for all conditions, RefFinder’s ranking results revealed that UBCE and EF1b ranked in the top 2, demonstrating the highest gene expression stability. In contrast, Cyb was positioned at the bottom of the comprehensive ranking table. This study not only revealed potential factors affecting the suitability of reference genes but also identified optimal reference genes from a set of candidate genes across diverse conditions. Full article

Background/Objectives: Tinnitus treatment is often based on coping strategies, as, still, no causal treatment is available. Based on our “Erlangen model” of tinnitus development, we treated tinnitus patients with individualized, non-masking low-intensity noise (LIN) to reduce the tinnitus loudness. Methods: A total of 72 adult patients with a tinnitus severity index below IV (tinnitus health questionnaire, THQ), a hearing loss not exceeding 40 dB up to 6 kHz, and without experience in hearing aid (HA) usage were included in this study. Their audiograms, tinnitus parameters, THQ scores, and optimal LIN were determined individually. Patients were double-blinded, assigned to a treatment-only (TO) or placebo-and-treatment (PT) group. The TO patients received treatment for four weeks while the PT patients initially received a placebo (low-intensity white noise) stimulation for two weeks and, hereinafter, treatment for four weeks. Every two weeks, the measurements listed above were repeated. The long-term effects on THQ were assessed four weeks after the end of treatment. The data were analyzed by parametric and nonparametric statistics. Results: We identified a significant decrease in the THQ score already after two (η² = 0.14) and four weeks of treatment (η² = 0.43), that was still present four weeks after the end of treatment (η² = 0.15) in the TO group. Subjective individual patient reports hint at a possible complete suppression of the percept by LINTS. The PT group profited much less, as the initial placebo treatment seemed to counteract the LIN effects. Conclusions: Individually fitted LIN treatment seems to be able to suppress tinnitus, in some cases completely. The optimal fitting of the LIN is crucial for treatment success. Full article

Despite substantial progress in medical care, acute myocarditis remains a life-threatening disorder with a sudden onset, often unexpectedly complicating a simple and common upper respiratory tract infection. In most cases, myocarditis is triggered by viral infections (over 80%), with an estimated incidence of 10–106 per 100,000 annually. The clinical course may worsen in cases of mixed etiology, where a primary viral infection is complicated by secondary bacterial pathogens, leading to prolonged inflammation and an increased risk of progression to chronic active myocarditis or dilated cardiomyopathy. We present a case report illustrating the clinical problem of acute myocarditis progression into a chronic active form. A central element of host defense is the inflammasome—an intracellular complex that activates pyroptosis and cytokine release (IL-1β, IL-18). While these processes help combat pathogens, their persistent activation may sustain inflammation and trigger heart failure and cardiac fibrosis, eventually leading to dilated cardiomyopathy. In this review, we summarize the current understanding of inflammasome pathways and their dual clinical role in myocarditis: they are essential for controlling acute infection but may become harmful when overactivated, contributing to chronic myocardial injury. Additionally, we discuss both novel and established therapeutic strategies targeting inflammatory and anti-fibrotic mechanisms, including IL-1 receptor blockers (anakinra, canakinumab), NOD-like receptor protein 3 (NLRP3) inhibitors (colchicine, MCC950, dapansutrile, INF200), NF-κB inhibitors, and angiotensin receptor-neprilysin inhibitors (ARNI), as well as microRNAs. Our aim is to emphasize the clinical importance of early identification of patients at risk of transitioning from acute to chronic inflammation, elucidate the role of inflammasomes, and present emerging therapies that may improve outcomes by balancing effective pathogen clearance with limitation of chronic cardiac damage. Full article